QA Lead, Robotics

Join us to shape the next frontier of AI-driven robotics! Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry. Dyna Robotics was founded by repeat founders Lindon Gao and York Yang, who sold Caper AI for $350 million, and former DeepMind research scientist Jason Ma. The company has raised over $140M, backed by top investors, including CRV and First Round.We're positioned to redefine the landscape of robotic automation. Position Overview Build and lead the QA function at Dyna across our full stack — cloud inference servers to Linux-based IoT devices on the factory floor. Real-world robotics cannot be fully validated in simulation; physical hardware behavior and real-environment variability require hands-on QA judgment. You will hire and mentor a QA team, define testing standards, and stay hands-on with critical validation — balancing automation with the physical testing our domain demands. We expect aggressive use of AI tools (LLM code generation, AI test assistants, GenAI test design) to multiply team output. What You’ll Do People Management: Hire, develop, and retain a QA team. Provide career development and feedback, delegate ownership to grow engineers, and monitor team energy to prevent burnout. Drive recruiting and foster a culture of intellectual honesty and psychological safety. QA Strategy & Cross-Functional Alignment: Own the QA strategy and roadmap. Align priorities with company vision, translate quality risks into business impact, and drive alignment across robotics, ML, and infrastructure teams. Shield

the team from organizational noise. Test Automation & CI/CD (primary focus): Build and maintain scalable automated test frameworks across robot software, cloud, and edge devices. Partner with developers to continuously expand CI coverage and shift testing left, minimizing manual testing over time. Release Validation & Benchmarking: Own and automate release checklists (inference server, station-side deployment). Run A/B testing for robotic tasks to measure success rates and latency. Manual validation where automation is not yet feasible. AI Model Evaluation & Regression Testing: Design and execute evaluation pipelines that validate AI model and full-system performance across model, software, and hardware updates. Define and track key metrics (task success rate, cycle time, generalization across environments), build automated regression benchmarks, and gate releases on evaluation results to prevent regressions from reaching production. System Monitoring & Alerting: Use tools like Datadog and Grafana to track system health, identify performance regressions, and maintain observability across production environments. Physical Hardware QA & Integration: Validate real-robot behavior that simulation cannot cover — mechanical repeatability, sensor calibration drift, and edge cases on physical hardware. Troubleshoot integration issues across firmware, drivers, and application software. Process & Documentation: Establish and maintain standard operating procedures (SOPs) for hardware and software validation; drive continuous process improvement. What You’ll Bring Education: Bachelor's or Master's in Computer Science, Robotics, or a related field. Experience: 7+ years in QA, SRE, or Robotics Engineering with hands-on experience testing real hardware systems (not purely software/simulation), including 2+ years in a team lead or management role. Technical Skills: Proficiency in Python. Comfortable navigating build systems, running test pipelines, and debugging failures across the stack — not expected to develop features. Systems: Strong Linux administration skills and experience with IoT/edge device deployments. Testing Tools: Deep experience in developing and managing automated testing frameworks; familiarity with modern AI/ML testing, model evaluation, and regression benchmarking techniques; simulation (Gazebo, MuJoCo), and CI/CD tools (Docker, Kubernetes). AI Tools: Track record of adopting AI/LLM tools to accelerate QA workflows (test generation, bug triage, automation scaffolding). Must actively seek out and integrate new AI tools. Hardware QA: Hands-on experience with physical robot testing, hardware bring-up, or manufacturing QA — understanding failure modes that only appear on real hardware. Soft Skills: Strong leadership and people management; effective communicator across technical and non-technical stakeholders.